In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\rd\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\rd\\final_road_output_2')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
# def find_max_building_storeys(gpr):
# max_building_storeys= 0
# if gpr >= 0 and gpr < 1.4:
# max_building_storeys = 5
# elif gpr >= 1.4 and gpr < 1.6:
# max_building_storeys = 12
# elif gpr >= 1.6 and gpr < 2.1:
# max_building_storeys = 24
# elif gpr >= 2.1 and gpr < 2.8:
# max_building_storeys = 36
# elif gpr >= 2.8:
# max_building_storeys = 48 ## by right got no limit
# return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [0,255,0]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [0,0,0]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,255]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]
gprs =[]
generated_gprs =[]
sanity_ratios =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
storey = matched_row['storeys'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
storey = int(storey)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = masked_rgb(simplified_gpr_value)
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = [255, 10, 169] # pink
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
# accuracy
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
#max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*storey/actual_site_area
gprs.append(gpr_value)
generated_gprs.append(generated_gpr)
# if generated_gpr == 0:
# accuracy = 0
# else:
# accuracy = (gpr_value - generated_gpr) / gpr_value #gpr_value is the target gpr
# loss =
# images.append(image_file)
# absolute_accuracy = abs(accuracy)
# absolute_accuracies.append(absolute_accuracy)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratios.append(ratio)
total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
absolute_error.append(abs(tar_gpr-gen_gpr))
square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 23065.1, Building pixels: 4716, Mask pixels: 16203, Generated GPR: 1.455286058137382
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12, Site area: 37265.0, Building pixels: 1847, Mask pixels: 27286, Generated GPR: 0.8122846881184491
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36, Site area: 10414.2, Building pixels: 211, Mask pixels: 8468, Generated GPR: 0.8970240906943789
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12, Site area: 6157.3, Building pixels: 995, Mask pixels: 4749, Generated GPR: 2.5142135186355024
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15, Site area: 19547.0, Building pixels: 3220, Mask pixels: 14230, Generated GPR: 3.3942375263527755
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 17455.9, Building pixels: 3839, Mask pixels: 12216, Generated GPR: 1.5712999345121152
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15, Site area: 22094.4, Building pixels: 1189, Mask pixels: 16171, Generated GPR: 1.1029002535402883
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 10097.1, Building pixels: 1814, Mask pixels: 7571, Generated GPR: 4.0731739532426365
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13564.8, Building pixels: 2509, Mask pixels: 9875, Generated GPR: 4.319291139240506
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18, Site area: 27418.2, Building pixels: 3291, Mask pixels: 21711, Generated GPR: 2.7284786513748793
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:13, Site area: 17940.2, Building pixels: 1173, Mask pixels: 11713, Generated GPR: 1.30188679245283
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:17, Site area: 13877.2, Building pixels: 689, Mask pixels: 9260, Generated GPR: 1.2649028077753781
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 7255.7, Building pixels: 1758, Mask pixels: 5237, Generated GPR: 1.6784418560244416
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:19, Site area: 10502.8, Building pixels: 1432, Mask pixels: 8220, Generated GPR: 3.309975669099756
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Storeys:18, Site area: 13000.3, Building pixels: 2430, Mask pixels: 9133, Generated GPR: 4.789225884156355
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13241.8, Building pixels: 2780, Mask pixels: 9581, Generated GPR: 4.93267926103747
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:16, Site area: 39401.6, Building pixels: 4210, Mask pixels: 28557, Generated GPR: 2.358791189550723
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:15, Site area: 28692.65, Building pixels: 2869, Mask pixels: 20400, Generated GPR: 2.109558823529412
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:16, Site area: 18747.8, Building pixels: 2736, Mask pixels: 12952, Generated GPR: 3.3798641136504015
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Storeys:19, Site area: 14344.0, Building pixels: 2161, Mask pixels: 10424, Generated GPR: 3.938891020721412 Accuracies:[0.03949004152670157, 0.6750861247526203, 0.6796342533234361, 0.5713834491471889, 0.1314125087842585, 0.1223570960800824, 0.6061070523070399, 0.35772465108087886, 0.43976371308016865, 0.299275548273752, 0.5350404312668464, 0.5482489972230792, 0.19888704001745838, 0.5761788900475029, 0.3683502526161015, 0.6442264203458233, 0.12323389978605861, 0.004551820728291406, 0.1266213712168005, 0.15849735903570947] Square error:[0.003056548224370003, 2.8483829739594406, 3.6213173113975548, 0.8357863576559058, 0.15542322718475537, 0.029343667563855005, 2.8801475494336173, 1.1517023339184287, 1.7405291100785123, 0.394985415233987, 2.24434318262727, 2.356523389575917, 0.07752986718633594, 1.4640411198134025, 1.6621033803787362, 3.7352491260443417, 0.0669728797890783, 9.137110726643958e-05, 0.14429674483940516, 0.2904035322141655] Absolute error:[0.05528605813738219, 1.6877153118815509, 1.9029759093056209, 0.9142135186355023, 0.39423752635277554, 0.17129993451211534, 1.6970997464597115, 1.0731739532426365, 1.319291139240506, 0.6284786513748792, 1.49811320754717, 1.5350971922246217, 0.2784418560244417, 1.209975669099756, 1.2892258841563553, 1.9326792610374701, 0.2587911895507231, 0.009558823529411953, 0.3798641136504015, 0.5388910207214122] Accuracy:0.36030354603199 MAE:0.9387204983342222 RMSE:1.1336275642429117